CUDA并行计算框架编程+矩阵相乘并行计算

清疚 2022-05-25 21:06 213阅读 0赞

当下的GPGPU(General Purpose GPU(Graphic Process Unit))—（CUDA: Compute Unified Device Architecture）即通用计算图形处理器。

安装过程可参考我的另一篇blog：[https://blog.csdn.net/pnan222/article/details/79449923][https_blog.csdn.net_pnan222_article_details_79449923]

CUDA软件架构：（1）开发库（CUDA Library）（2）运行时环境（CUDA Runtime）（3）驱动（CUDA Driver）

CUDA的线程层次结构：Kenel-->Grid-->Block-->Thread

GPU硬件的一个核心组件是SM（Streaming Multiprocessor）流式多处理器，SM可以并发地执行数百个线程，且一个block对应一个SM，而一个SM则可以对应多个block

grid只是逻辑层；而SM才是真正的物理层；block的大小一般要设置成32的倍数

在VS上的配置过程可参考blog：[https://blog.csdn.net/kyocen/article/details/51424161][https_blog.csdn.net_kyocen_article_details_51424161]

Code:

#include <iostream>  
    #include <assert.h>
    #include <time.h>
    #include <Windows.h>
    //#include <mmsystem.h>
    #include "cuda_runtime.h"  
    #include "device_launch_parameters.h"  
    #define DWORD unsigned long
    #pragma comment(lib,"winmm.lib")
    
    using namespace std;
    
    __global__ void add(float* a, float* b, float* c, int N)
    {
    	for (int i = 0; i < N; i++)
    	{
    		c[i] = a[i] + b[i];
    	}
    }
    
    
    int main() {
    	int N = 1 << 15;
    	int nBytes = N * sizeof(float);
    	//申请host内存
    	float *x, *y, *z;
    	x = (float*)malloc(nBytes);
    	y = (float*)malloc(nBytes);
    	z = (float*)malloc(nBytes);
    
    	//初始化数据
    	for (int i = 0; i < N; i++)
    	{
    		x[i] = 10.0;
    		y[i] = 20.0;
    	}
    
    	//申请device内存
    	float *d_x, *d_y, *d_z;
    	cudaMalloc((void**)&d_x, nBytes);
    	cudaMalloc((void**)&d_y, nBytes);
    	cudaMalloc((void**)&d_z, nBytes);
    
    	//将host数据拷贝到device
    	cudaMemcpy((void*)d_x, (void*)x, nBytes, cudaMemcpyHostToDevice);
    	cudaMemcpy((void*)d_y, (void*)y, nBytes, cudaMemcpyHostToDevice);
    
    
    	//定义kernel的执行配置
    	dim3 blockSize(256);
    	dim3 gridSize((int)((N + blockSize.x - 1) / blockSize.x));
    
    	//编写计时函数
    	DWORD t1, t2;
    	t1 = timeGetTime();
    	
    	
    
    	//执行kernel
    	add << < gridSize, blockSize >> >(d_x, d_y, d_z, N);
    
    	t2 = timeGetTime();
    	printf("Use Time:%f (s)\n", (t2 - t1)*1.0 / 1000);
    
    	//将device得到的结果拷贝到host
    	cudaMemcpy((void*)z, (void*)d_z, nBytes, cudaMemcpyDeviceToHost);
    
    	//检查执行结果
    	float maxError = 0.0;
    	for (int i = 0; i < N; i++)
    	{
    		maxError = fmax(maxError, fabs(z[i] - 30.0));
    	}
    	cout << "最大误差：" << maxError << endl;
    
    	//释放device内存
    	cudaFree(d_x);
    	cudaFree(d_y);
    	cudaFree(d_z);
    
    	//释放host内存
    	free(x);
    	free(y);
    	free(z);
    	
    	system("pause");
    	return 0;
    }

实现矩阵相乘并行计算的代码：

#include <iostream>  
    #include <assert.h>
    #include <time.h>
    #include <Windows.h>
    //#include <mmsystem.h>
    #include "cuda_runtime.h"  
    #include "device_launch_parameters.h"  
    #define DWORD unsigned long
    #pragma comment(lib,"winmm.lib")
    
    using namespace std;
    
    __global__ void add(float* a, float* b, float* c, int N)
    {
    	for (int i = 0; i < N; i++)
    	{
    		c[i] = a[i] + b[i];
    	}
    }
    
    __global__ void MatrixMuiOnDevice(int *M, int *N, int *P, int width)
    {
    	int x = threadIdx.x;
    	int y = threadIdx.y; //获取该线程的位置
    
    	float Pervalue = 0;
    
    	for (int i = 0; i < width; i++)
    	{
    		float Mdlement = M[y * width + i];
    		float Ndlement = N[width * i + x];
    
    		Pervalue += Mdlement * Ndlement;
    	}
    
    	P[y * width + x] = Pervalue;
    }
    
    int main() {
    
    	int a[30][30], b[30][30], c[30][30];
    	int *M, *N, *P;
    	int width = 30;
    	int NUM = 900;
    	dim3 dimBlock(30, 30);
    	cudaEvent_t start, stop;
    	float elapsedTime;
    	cudaEventCreate(&start);
    	cudaEventCreate(&stop);
    
    
    	cudaMalloc((void**)&M, 900 * sizeof(int));
    	cudaMalloc((void**)&N, 900 * sizeof(int));
    	cudaMalloc((void**)&P, 900 * sizeof(int));
    	//初始化
    	for (int i = 0; i < 30; i++)
    	{
    		for (int j = 0; j < 30; j++)
    		{
    			a[i][j] = 2;
    			b[i][j] = 3;
    		}
    	}
    	cudaMemcpy(M, a, NUM * sizeof(int), cudaMemcpyHostToDevice);
    	cudaMemcpy(N, b, NUM * sizeof(int), cudaMemcpyHostToDevice);
    	
    	cudaEventRecord(start, 0);
    	MatrixMuiOnDevice << <1, dimBlock >> >(M, N, P, width);
    	cudaThreadSynchronize();
    
    	cudaEventRecord(stop, 0);
    	cudaEventSynchronize(stop);
    	cudaEventElapsedTime(&elapsedTime, start, stop);
    
    	printf("%f\n", elapsedTime);
    
    	cudaMemcpy(c, P, NUM * sizeof(int), cudaMemcpyDeviceToHost);
    	for (int i = 0; i < 30; i++)
    	{
    		for (int j = 0; j < 30; j++)
    		{
    			printf("%d ", c[i][j]);
    		}
    		cout << endl;
    	}
    	cudaFree(M);
    	cudaFree(N);
    	cudaFree(P);
    	
    	system("pause");
    	return 0;
    }

[https_blog.csdn.net_pnan222_article_details_79449923]: https://blog.csdn.net/pnan222/article/details/79449923
[https_blog.csdn.net_kyocen_article_details_51424161]: https://blog.csdn.net/kyocen/article/details/51424161